



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a      :  int			     =			16
a      :  str			     =			32







def  lowerCAmelCase_	(lowerCAmelCase__:		Accelerator		,						lowerCAmelCase__:		int = 1_6		,						lowerCAmelCase__:		str = "bert-base-cased"  ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple							      =     AutoTokenizer.from_pretrained(lowerCAmelCase__  )
			UpperCAmelCase_:		Optional[int]							      =     load_dataset("""glue"""		,						"""mrpc"""  )

			def tokenize_function(lowerCAmelCase__:		str  ):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_:		List[str]							      =     tokenizer(examples["""sentence1"""]		,						examples["""sentence2"""]		,						truncation=lowerCAmelCase__		,						max_length=lowerCAmelCase__  )
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_:		List[str]							      =     datasets.map(
			    lowerCAmelCase__		,						batched=lowerCAmelCase__		,						remove_columns=["""idx""", """sentence1""", """sentence2"""]		,						load_from_cache_file=lowerCAmelCase__  )

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_:		Optional[int]							      =     tokenized_datasets.rename_column("""label"""		,						"""labels"""  )

			def collate_fn(lowerCAmelCase__:		Dict  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(lowerCAmelCase__		,						padding="""max_length"""		,						max_length=1_2_8		,						return_tensors="""pt"""  )
						return tokenizer.pad(lowerCAmelCase__		,						padding="""longest"""		,						return_tensors="""pt"""  )

			# Instantiate dataloaders.
			UpperCAmelCase_:		Union[str, Any]							      =     DataLoader(
			    tokenized_datasets["""train"""]		,						shuffle=lowerCAmelCase__		,						collate_fn=lowerCAmelCase__		,						batch_size=lowerCAmelCase__  )
			UpperCAmelCase_:		Optional[int]							      =     DataLoader(
			    tokenized_datasets["""validation"""]		,						shuffle=lowerCAmelCase__		,						collate_fn=lowerCAmelCase__		,						batch_size=lowerCAmelCase__  )

			return train_dataloader, eval_dataloader







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		int  ):





			"""simple docstring"""


			UpperCAmelCase_:		int							      =     Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_:		List[str]							      =     config["""lr"""]
			UpperCAmelCase_:		Dict							      =     int(config["""num_epochs"""]  )
			UpperCAmelCase_:		Optional[int]							      =     int(config["""seed"""]  )
			UpperCAmelCase_:		Optional[int]							      =     int(config["""batch_size"""]  )
			UpperCAmelCase_:		int							      =     args.model_name_or_path

			set_seed(lowerCAmelCase__  )
			UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     get_dataloaders(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_:		Dict							      =     AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__		,						return_dict=lowerCAmelCase__  )

			# Instantiate optimizer
			UpperCAmelCase_:		Dict							      =     (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_:		Optional[Any]							      =     optimizer_cls(params=model.parameters()		,						lr=lowerCAmelCase__  )

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_:		int							      =     accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						UpperCAmelCase_:		Tuple							      =     1
			UpperCAmelCase_:		Any							      =     (len(lowerCAmelCase__  ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_:		Optional[int]							      =     get_linear_schedule_with_warmup(
						    optimizer=lowerCAmelCase__		,						num_warmup_steps=0		,						num_training_steps=lowerCAmelCase__		,						)
			else:
						UpperCAmelCase_:		Any							      =     DummyScheduler(lowerCAmelCase__		,						total_num_steps=lowerCAmelCase__		,						warmup_num_steps=0  )

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     accelerator.prepare(
			    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_:		Optional[Any]							      =     0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_:		Dict							      =     0

			# Now we train the model
			UpperCAmelCase_:		int							      =     evaluate.load("""glue"""		,						"""mrpc"""  )
			UpperCAmelCase_:		str							      =     0
			UpperCAmelCase_:		Any							      =     {}
			for epoch in range(lowerCAmelCase__		,						lowerCAmelCase__  ):
						model.train()
						for step, batch in enumerate(lowerCAmelCase__  ):
									UpperCAmelCase_:		Any							      =     model(**lowerCAmelCase__  )
									UpperCAmelCase_:		Optional[Any]							      =     outputs.loss
									UpperCAmelCase_:		str							      =     loss / gradient_accumulation_steps
									accelerator.backward(lowerCAmelCase__  )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						UpperCAmelCase_:		List[str]							      =     0
						for step, batch in enumerate(lowerCAmelCase__  ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device  )
									with torch.no_grad():
												UpperCAmelCase_:		Optional[int]							      =     model(**lowerCAmelCase__  )
									UpperCAmelCase_:		Optional[Any]							      =     outputs.logits.argmax(dim=-1  )
									# It is slightly faster to call this once, than multiple times
									UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     accelerator.gather(
									    (predictions, batch["""labels"""])  )  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(lowerCAmelCase__  ) - 1:
															UpperCAmelCase_:		Any							      =     predictions[: len(eval_dataloader.dataset  ) - samples_seen]
															UpperCAmelCase_:		Tuple							      =     references[: len(eval_dataloader.dataset  ) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=lowerCAmelCase__		,						references=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[int]							      =     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'		,						lowerCAmelCase__  )
						UpperCAmelCase_:		List[Any]							      =     eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									UpperCAmelCase_:		List[str]							      =     eval_metric["""accuracy"""]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir		,						"""all_results.json"""  )		,						"""w"""  ) as f:
									json.dump(lowerCAmelCase__		,						lowerCAmelCase__  )







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""  )
			parser.add_argument(
			    """--model_name_or_path"""		,						type=lowerCAmelCase__		,						default="""bert-base-cased"""		,						help="""Path to pretrained model or model identifier from huggingface.co/models."""		,						required=lowerCAmelCase__		,						)
			parser.add_argument(
			    """--output_dir"""		,						type=lowerCAmelCase__		,						default="""."""		,						help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,						)
			parser.add_argument(
			    """--performance_lower_bound"""		,						type=lowerCAmelCase__		,						default=lowerCAmelCase__		,						help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""		,						)
			parser.add_argument(
			    """--num_epochs"""		,						type=lowerCAmelCase__		,						default=3		,						help="""Number of train epochs."""		,						)
			UpperCAmelCase_:		str							      =     parser.parse_args()
			UpperCAmelCase_:		str							      =     {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
			training_function(lowerCAmelCase__		,						lowerCAmelCase__  )


if __name__ == "__main__":
	main()











def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






